In a complex and changing world, current scientific approaches to problem solving have drastically evolved to include complexity models and emerging systems. Breaking problems into the smallest component and examining its position inside a system allows for a more regulated and measured technique in investigation, discovery, and providing solutions. Systems Research for Real-World Challenges is an essential reference source that explores the development of systems philosophy, theory, practice, its models, concepts, and methodologies developed as an aid for improving decision making and problem solving for the benefit of organizations and society as a whole. Featuring coverage on a broad range of topics such as complexity models, management systems, and economic policy, this book is ideally designed for scientists, policy makers, researchers, managers, and systematists seeking current research on the benefits and approaches of problem solving within the realm of systems thinking and practice.

In the digital era, novel applications and techniques in the realm of computer science are increasing constantly. These innovations have led to new techniques and developments in the field of cybernetics. The Handbook of Research on Applied Cybernetics and Systems Science is an authoritative reference publication for the latest scholarly information on complex concepts of more adaptive and self-regulating systems. Featuring exhaustive coverage on a variety of topics such as infectious disease modeling, clinical imaging, and computational modeling, this publication is an ideal source for researchers and students in the field of computer science seeking emerging trends in computer science and computational mathematics.

This book focuses on the emergence of creative ideas from cognitive and social dynamics. In particular, it presents data, models, and analytical methods grounded in a network dynamics approach. It has long been hypothesized that innovation arises from a recombination of older ideas and concepts, but this has been studied primarily at an abstract level. In this book, we consider the networks underlying innovation - from the brain networks supporting semantic cognition to human networks such as brainstorming groups or individuals interacting through social networks - and relate the emergence of ideas to the structure and dynamics of these networks. Methods described include experimental studies with human participants, mathematical evaluation of novelty from group brainstorming experiments, neurodynamical modeling of conceptual combination, and multi-agent modeling of collective creativity. The main distinctive features of this book are the breadth of perspectives considered, the integration of experiments with theory, and a focus on the combinatorial emergence of ideas.

This unique book gathers various scientific and mathematical approaches to and descriptions of the natural and physical world stemming from a broad range of mathematical areas - from model systems, differential equations, statistics, and probability - all of which scientifically and mathematically reveal the inherent beauty of natural and physical phenomena. Topics include Archimedean and Non-Archimedean approaches to mathematical modeling; thermography model with application to tungiasis inflammation of the skin; modeling of a tick-Killing Robot; various aspects of the mathematics for Covid-19, from simulation of social distancing scenarios to the evolution dynamics of the coronavirus in some given tropical country to the spatiotemporal modeling of the progression of the pandemic. Given its scope and approach, the book will benefit researchers and students of mathematics, the sciences and engineering, and everyone else with an appreciation for the beauty of nature. The outcome is a mathematical enrichment of nature's beauty in its various manifestations. This volume honors Dr. John Adam, a Professor at Old Dominion University, USA, for his lifetime achievements in the fields of mathematical modeling and applied mathematics. Dr. Adam has published over 110 papers and authored several books.

What causes one system to break down and another to rebound? Are we merely subject to the whim of forces beyond our control? Or, in the face of constant disruption, can we build better shock absorbers--for ourselves, our communities, our economies, and for the planet as a whole?
Reporting firsthand from the coral reefs of Palau to the back streets of Palestine, Andrew Zolli and Ann Marie Healy relate breakthrough scientific discoveries, pioneering social and ecological innovations, and important new approaches to constructing a more resilient world. Zolli and Healy show how this new concept of resilience is a powerful lens through which we can assess major issues afresh: from business planning to social develop-ment, from urban planning to national energy security--circumstances that affect us all.
Provocative, optimistic, and eye-opening, Resilience sheds light on why some systems, people, and communities fall apart in the face of disruption and, ultimately, how they can learn to bounce back.

This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: deep learning; artificial intelligence; applications of game theory; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.

This monograph provides a comprehensive exploration of new tools for modelling, analysis, and control of networked dynamical systems. Expanding on the authors' previous work, this volume highlights how local exchange of information and cooperation among neighboring agents can lead to emergent global behaviors in a given networked dynamical system. Divided into four sections, the first part of the book begins with some preliminaries and the general networked dynamical model that is used throughout the rest of the book. The second part focuses on synchronization of networked dynamical systems, synchronization with non-expansive dynamics, periodic solutions of networked dynamical systems, and modulus consensus of cooperative-antagonistic networks. In the third section, the authors solve control problems with input constraint, large delays, and heterogeneous dynamics. The final section of the book is devoted to applications, studying control problems of spacecraft formation flying, multi-robot rendezvous, and energy resource coordination of power networks. Modelling, Analysis, and Control of Networked Dynamical Systems will appeal to researchers and graduate students interested in control theory and its applications, particularly those working in networked control systems, multi-agent systems, and cyber-physical systems. This volume can also be used in advanced undergraduate and graduate courses on networked control systems and multi-agent systems.

The past few years have seen the attention and rapid developments in event-triggered sampled-data systems, in which the effect of event-triggered sensor measurements and controller updates is explored in controller analysis and design. This book offers the first systematic treatment of event-triggered sampled-data control system design using active disturbance rejection control (ADRC), an effective approach that is popular in both theoretic research and industrial applications. Extensive application examples with numerous illustrations are included to show how the event-triggered ADRC with theoretic performance guarantees can be implemented in engineering systems and how the performance can be actually achieved. For theoretic researchers and graduate students, the presented results provide new directions in theoretic research on event-triggered sampled-data systems; for control practitioners, the book offers an effective approach to achieving satisfactory performance with limited sampling rates.